234 lines
8.2 KiB
C++
234 lines
8.2 KiB
C++
// * jidctflt.c
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// *
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// * Copyright (C) 1994, Thomas G. Lane.
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// * This file is part of the Independent JPEG Group's software.
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// * For conditions of distribution and use, see the accompanying README file.
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// *
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// * This file contains a floating-point implementation of the
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// * inverse DCT (Discrete Cosine Transform). In the IJG code, this routine
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// * must also perform dequantization of the input coefficients.
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// *
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// * This implementation should be more accurate than either of the integer
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// * IDCT implementations. However, it may not give the same results on all
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// * machines because of differences in roundoff behavior. Speed will depend
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// * on the hardware's floating point capacity.
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// *
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// * A 2-D IDCT can be done by 1-D IDCT on each column followed by 1-D IDCT
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// * on each row (or vice versa, but it's more convenient to emit a row at
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// * a time). Direct algorithms are also available, but they are much more
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// * complex and seem not to be any faster when reduced to code.
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// *
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// * This implementation is based on Arai, Agui, and Nakajima's algorithm for
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// * scaled DCT. Their original paper (Trans. IEICE E-71(11):1095) is in
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// * Japanese, but the algorithm is described in the Pennebaker & Mitchell
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// * JPEG textbook (see REFERENCES section in file README). The following code
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// * is based directly on figure 4-8 in P&M.
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// * While an 8-point DCT cannot be done in less than 11 multiplies, it is
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// * possible to arrange the computation so that many of the multiplies are
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// * simple scalings of the final outputs. These multiplies can then be
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// * folded into the multiplications or divisions by the JPEG quantization
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// * table entries. The AA&N method leaves only 5 multiplies and 29 adds
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// * to be done in the DCT itself.
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// * The primary disadvantage of this method is that with a fixed-point
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// * implementation, accuracy is lost due to imprecise representation of the
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// * scaled quantization values. However, that problem does not arise if
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// * we use floating point arithmetic.
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#define JPEG_INTERNALS
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#include "JINCLUDE.h"
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#include "JPEGLIB.h"
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#include "JDCT.h" // Private declarations for DCT subsystem
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#ifdef DCT_FLOAT_SUPPORTED
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// * This module is specialized to the case DCTSIZE = 8.
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#if DCTSIZE != 8
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Sorry, this code only copes with 8x8 DCTs. // deliberate syntax err
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#endif
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// Dequantize a coefficient by multiplying it by the multiplier-table
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// * entry; produce a float result.
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#define DEQUANTIZE(coef,quantval) (((FAST_FLOAT) (coef)) * (quantval))
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// * Perform dequantization and inverse DCT on one block of coefficients.
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GLOBAL void
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jpeg_idct_float (j_decompress_ptr cinfo, jpeg_component_info * compptr,
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JCOEFPTR coef_block,
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JSAMPARRAY output_buf, JDIMENSION output_col)
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{
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FAST_FLOAT tmp0, tmp1, tmp2, tmp3, tmp4, tmp5, tmp6, tmp7;
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FAST_FLOAT tmp10, tmp11, tmp12, tmp13;
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FAST_FLOAT z5, z10, z11, z12, z13;
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JCOEFPTR inptr;
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FLOAT_MULT_TYPE * quantptr;
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FAST_FLOAT * wsptr;
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JSAMPROW outptr;
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JSAMPLE *range_limit = IDCT_range_limit(cinfo);
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int ctr;
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FAST_FLOAT workspace[DCTSIZE2]; // buffers data between passes
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SHIFT_TEMPS
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// Pass 1: process columns from input, store into work array.
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inptr = coef_block;
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quantptr = (FLOAT_MULT_TYPE *) compptr->dct_table;
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wsptr = workspace;
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for (ctr = DCTSIZE; ctr > 0; ctr--) {
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// Due to quantization, we will usually find that many of the input
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// * coefficients are zero, especially the AC terms. We can exploit this
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// * by short-circuiting the IDCT calculation for any column in which all
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// * the AC terms are zero. In that case each output is equal to the
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// * DC coefficient (with scale factor as needed).
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// * With typical images and quantization tables, half or more of the
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// * column DCT calculations can be simplified this way.
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if ((inptr[DCTSIZE*1] | inptr[DCTSIZE*2] | inptr[DCTSIZE*3] |
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inptr[DCTSIZE*4] | inptr[DCTSIZE*5] | inptr[DCTSIZE*6] |
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inptr[DCTSIZE*7]) == 0) {
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// AC terms all zero
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FAST_FLOAT dcval = DEQUANTIZE(inptr[DCTSIZE*0], quantptr[DCTSIZE*0]);
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wsptr[DCTSIZE*0] = dcval;
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wsptr[DCTSIZE*1] = dcval;
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wsptr[DCTSIZE*2] = dcval;
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wsptr[DCTSIZE*3] = dcval;
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wsptr[DCTSIZE*4] = dcval;
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wsptr[DCTSIZE*5] = dcval;
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wsptr[DCTSIZE*6] = dcval;
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wsptr[DCTSIZE*7] = dcval;
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inptr++; // advance pointers to next column
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quantptr++;
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wsptr++;
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continue;
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}
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// Even part
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tmp0 = DEQUANTIZE(inptr[DCTSIZE*0], quantptr[DCTSIZE*0]);
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tmp1 = DEQUANTIZE(inptr[DCTSIZE*2], quantptr[DCTSIZE*2]);
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tmp2 = DEQUANTIZE(inptr[DCTSIZE*4], quantptr[DCTSIZE*4]);
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tmp3 = DEQUANTIZE(inptr[DCTSIZE*6], quantptr[DCTSIZE*6]);
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tmp10 = tmp0 + tmp2; // phase 3
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tmp11 = tmp0 - tmp2;
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tmp13 = tmp1 + tmp3; // phases 5-3
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tmp12 = (tmp1 - tmp3) * ((FAST_FLOAT) 1.414213562) - tmp13; // 2*c4
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tmp0 = tmp10 + tmp13; // phase 2
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tmp3 = tmp10 - tmp13;
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tmp1 = tmp11 + tmp12;
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tmp2 = tmp11 - tmp12;
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// Odd part
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tmp4 = DEQUANTIZE(inptr[DCTSIZE*1], quantptr[DCTSIZE*1]);
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tmp5 = DEQUANTIZE(inptr[DCTSIZE*3], quantptr[DCTSIZE*3]);
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tmp6 = DEQUANTIZE(inptr[DCTSIZE*5], quantptr[DCTSIZE*5]);
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tmp7 = DEQUANTIZE(inptr[DCTSIZE*7], quantptr[DCTSIZE*7]);
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z13 = tmp6 + tmp5; // phase 6
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z10 = tmp6 - tmp5;
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z11 = tmp4 + tmp7;
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z12 = tmp4 - tmp7;
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tmp7 = z11 + z13; // phase 5
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tmp11 = (z11 - z13) * ((FAST_FLOAT) 1.414213562); // 2*c4
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z5 = (z10 + z12) * ((FAST_FLOAT) 1.847759065); // 2*c2
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tmp10 = ((FAST_FLOAT) 1.082392200) * z12 - z5; // 2*(c2-c6)
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tmp12 = ((FAST_FLOAT) -2.613125930) * z10 + z5; // -2*(c2+c6)
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tmp6 = tmp12 - tmp7; // phase 2
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tmp5 = tmp11 - tmp6;
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tmp4 = tmp10 + tmp5;
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wsptr[DCTSIZE*0] = tmp0 + tmp7;
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wsptr[DCTSIZE*7] = tmp0 - tmp7;
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wsptr[DCTSIZE*1] = tmp1 + tmp6;
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wsptr[DCTSIZE*6] = tmp1 - tmp6;
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wsptr[DCTSIZE*2] = tmp2 + tmp5;
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wsptr[DCTSIZE*5] = tmp2 - tmp5;
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wsptr[DCTSIZE*4] = tmp3 + tmp4;
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wsptr[DCTSIZE*3] = tmp3 - tmp4;
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inptr++; // advance pointers to next column
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quantptr++;
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wsptr++;
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}
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// Pass 2: process rows from work array, store into output array.
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// Note that we must descale the results by a factor of 8 == 2**3.
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wsptr = workspace;
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for (ctr = 0; ctr < DCTSIZE; ctr++) {
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outptr = output_buf[ctr] + output_col;
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// Rows of zeroes can be exploited in the same way as we did with columns.
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// * However, the column calculation has created many nonzero AC terms, so
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// * the simplification applies less often (typically 5% to 10% of the time).
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// * And testing floats for zero is relatively expensive, so we don't bother.
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// Even part
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tmp10 = wsptr[0] + wsptr[4];
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tmp11 = wsptr[0] - wsptr[4];
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tmp13 = wsptr[2] + wsptr[6];
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tmp12 = (wsptr[2] - wsptr[6]) * ((FAST_FLOAT) 1.414213562) - tmp13;
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tmp0 = tmp10 + tmp13;
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tmp3 = tmp10 - tmp13;
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tmp1 = tmp11 + tmp12;
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tmp2 = tmp11 - tmp12;
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// Odd part
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z13 = wsptr[5] + wsptr[3];
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z10 = wsptr[5] - wsptr[3];
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z11 = wsptr[1] + wsptr[7];
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z12 = wsptr[1] - wsptr[7];
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tmp7 = z11 + z13;
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tmp11 = (z11 - z13) * ((FAST_FLOAT) 1.414213562);
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z5 = (z10 + z12) * ((FAST_FLOAT) 1.847759065); // 2*c2
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tmp10 = ((FAST_FLOAT) 1.082392200) * z12 - z5; // 2*(c2-c6)
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tmp12 = ((FAST_FLOAT) -2.613125930) * z10 + z5; // -2*(c2+c6)
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tmp6 = tmp12 - tmp7;
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tmp5 = tmp11 - tmp6;
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tmp4 = tmp10 + tmp5;
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// Final output stage: scale down by a factor of 8 and range-limit
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outptr[0] = range_limit[(int) DESCALE((INT32) (tmp0 + tmp7), 3)
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& RANGE_MASK];
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outptr[7] = range_limit[(int) DESCALE((INT32) (tmp0 - tmp7), 3)
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& RANGE_MASK];
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outptr[1] = range_limit[(int) DESCALE((INT32) (tmp1 + tmp6), 3)
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& RANGE_MASK];
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outptr[6] = range_limit[(int) DESCALE((INT32) (tmp1 - tmp6), 3)
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& RANGE_MASK];
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outptr[2] = range_limit[(int) DESCALE((INT32) (tmp2 + tmp5), 3)
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& RANGE_MASK];
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outptr[5] = range_limit[(int) DESCALE((INT32) (tmp2 - tmp5), 3)
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& RANGE_MASK];
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outptr[4] = range_limit[(int) DESCALE((INT32) (tmp3 + tmp4), 3)
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& RANGE_MASK];
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outptr[3] = range_limit[(int) DESCALE((INT32) (tmp3 - tmp4), 3)
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& RANGE_MASK];
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wsptr += DCTSIZE; // advance pointer to next row
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}
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}
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#endif // DCT_FLOAT_SUPPORTED
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